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Algorithms Course - Graph Theory Tutorial from a Google Engineer

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Overview

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Dive into a comprehensive 7-hour course on Graph Theory algorithms, taught by a Google engineer. Master essential programming skills through in-depth explanations of crucial algorithms, accompanied by working Java source code. Explore topics ranging from Depth First Search and Breadth First Search to advanced concepts like Dijkstra's Shortest Path, Tarjan's Strongly Connected Components, and Network Flow algorithms. Learn how to solve complex problems such as the Travelling Salesman Problem using dynamic programming. Gain practical insights into real-world applications with examples like the Mice and Owls problem. Access supplementary materials including GitHub repositories for code and slides to enhance your learning experience. By the end of this course, develop a strong foundation in Graph Theory algorithms, essential for becoming an exceptional programmer.

Syllabus

) Graph Theory Introduction.
) Problems in Graph Theory.
) Depth First Search Algorithm.
) Breadth First Search Algorithm.
) Breadth First Search grid shortest path.
) Topological Sort Algorithm.
) Shortest/Longest path on a Directed Acyclic Graph (DAG).
) Dijkstra's Shortest Path Algorithm.
) Dijkstra's Shortest Path Algorithm | Source Code.
) Bellman Ford Algorithm.
) Floyd Warshall All Pairs Shortest Path Algorithm.
) Floyd Warshall All Pairs Shortest Path Algorithm | Source Code.
) Bridges and Articulation points Algorithm.
) Bridges and Articulation points source code.
) Tarjans Strongly Connected Components algorithm.
) Tarjans Strongly Connected Components algorithm source code.
) Travelling Salesman Problem | Dynamic Programming.
) Travelling Salesman Problem source code | Dynamic Programming.
) Existence of Eulerian Paths and Circuits.
) Eulerian Path Algorithm.
) Eulerian Path Algorithm | Source Code.
) Prim's Minimum Spanning Tree Algorithm.
) Eager Prim's Minimum Spanning Tree Algorithm.
) Eager Prim's Minimum Spanning Tree Algorithm | Source Code.
) Max Flow Ford Fulkerson | Network Flow.
) Max Flow Ford Fulkerson | Source Code.
) Unweighted Bipartite Matching | Network Flow.
) Mice and Owls problem | Network Flow.
) Elementary Math problem | Network Flow.
) Edmonds Karp Algorithm | Network Flow.
) Edmonds Karp Algorithm | Source Code.
) Capacity Scaling | Network Flow.
) Capacity Scaling | Network Flow | Source Code.
) Dinic's Algorithm | Network Flow.
) Dinic's Algorithm | Network Flow | Source Code.

Taught by

freeCodeCamp.org

Reviews

5.0 rating, based on 2 Class Central reviews

Start your review of Algorithms Course - Graph Theory Tutorial from a Google Engineer

  • Profile image for Midhat
    Midhat @midhat
    6
    Really superb Course! Learn a bunch of cool stuff liek vertices, edges, computing graphs, BFS, Topological sort algorithm, DAG, Dijkstra's shortest path algo, bellman ford algo, Dinic's algorithm.
  • Profile image for Joao Vinicius Rodrigues Santos
    Joao Vinicius Rodrigues Santos
    recently completed the Algorithms Course - Graph Theory Tutorial from a Google Engineer by William Fiset, and it exceeded my expectations in every way. The course offers a remarkably thorough introduction to graph theory, covering everything from fundamental representations and common graph problems to advanced topics like shortest path algorithms, negative cycles, strongly connected components, and network flows.
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